Applications are invited for three tenure-track Assistant Professor positions with term of appointment to begin August 1, 2019.

Research & News

New ACM Officers Elected new

ACM Officers for the next school year were elected on April 25. The new officers will be:

President: Hannah Scheffe

Vice President: Andrew Bevelhymer

Secretary: Kayla Walkup

Treasurer: Brayden Dyke

Philanthropy/PR Chair: Ethan Mayberry

Students Win Prize for Health Data Innovation new

Our Computer Science PhD students Ashwin Viswanathan and Goutam Mylavarapu participated in the OSU Center for Health Sciences Innovation Health Data shootout from February 20 - March 14, 2018 and secured a 3rd place finish for their model predicting and improving patient care for people afflicted by Post Traumatic Stress Disorder. Congratulations to Ashwin and Goutam!

Dr. Chan-Tin, Dr. Crick, Dr. Etemadpour and Dr. George were awarded a National Science Foundation Research Experience for Undergraduates Site award. The REU program in the CS department at OSU is a ten-week summer program for undergraduate students. Participants in the program will contribute to research projects in big data analytics, including, robotics, computer vision, visualization, Twitter data analytics, anonymity, and machine learning.

We had a very successful programming contest on April 14, 2016 with 48 contestants and 133 correct submissions. First place went to James Robinson, with 7 problems solved out of 10. Second place went to Ziheng Lin, with 5 problems solved by 7:28. Third place went to Ben Meyers with 5 problems solved by 7:50. Fourth place went to Thomas Games with 5 problems solved by 7:57. You can see all the problems for this and previous contests at the site http://cs.okstate.edu/~clined/ -- thanks to everyone who participated as well as Dr. Cline for overseeing the contest!

Prediction is a critical aspect of any company. For an energy cooperative, predicting future energy demands, energy generation, and problems in energy transmission or production is important. Predicting the future requires data. The goal of this project is to enable this wide data collection and transmission back to the server so that predictions can be made. The accuracy of the predictions depends largely on the quality and amount of data. Most of the data collection are performed through sensors gathering information such as ambient information, power quality, and harmonics. Sensor data collection: Effective sensor data collection is a challenge because of the different types of sensors, different data formats and different sensor locations. Other challenges are the extensibility and modularity of the sensor hardware because the type of data collected can and will likely change in the future. Replacing the whole sensor infrastructure every few years is not desirable. The solution proposed is to design and deploy a new sensor infrastructure that will integrate all the sensors and aggregate all the data collection.

Many diseases are strongly correlated with and affected by ambient environment, such as temperature and ultraviolet. Some of these diseases like skin cancer are common and can be serious to result in death. Each year more than two million new cases of skin cancer are diagnosed in the U.S. Meanwhile, according to CDC WONDER, Oklahoma has the highest mortality rate for melanoma in the U.S. In order to enable better control and treatment of these diseases, it is desirable to integrate correlated ambient information into pervasive health monitoring systems, for both medical research and health-care services. In this project, the team will design and develop the first pervasive health monitoring system that integrates disease-correlated ambient factors. In addition, it will also surpass all existing ones by providing reliable, efficient and privacy-preserving data collection and transmission. At the completion of this project, it not only will greatly enhance the preventive, proactive and patient-centered treatment to many chronic diseases, but also can facilitate the research on discovering new correlations between ambient factors and disease development.